VideoLLaMA2 / videollama2 /eval /eval_video_qa_gpt.py
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import os
import ast
import json
import time
import argparse
import traceback
from tqdm import tqdm
from multiprocessing.pool import Pool
from openai import AzureOpenAI
def init():
client = AzureOpenAI(
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_KEY"),
api_version="2024-02-15-preview"
)
return client
def interaction(client, message_text):
completion = client.chat.completions.create(
model=os.getenv("AZURE_OPENAI_DEPLOYNAME"),
messages = message_text,
temperature=0.7,
max_tokens=800,
top_p=0.95,
frequency_penalty=0,
presence_penalty=0,
stop=None
)
return completion
def prompt_gpt(question, answer, pred, key, qa_set, output_dir):
message = [
{
"role": "system",
"content":
"You are an intelligent chatbot designed for evaluating the correctness of generative outputs for question-answer pairs. "
"Your task is to compare the predicted answer with the correct answer and determine if they match meaningfully. Here's how you can accomplish the task:"
"------"
"##INSTRUCTIONS: "
"- Focus on the meaningful match between the predicted answer and the correct answer.\n"
"- Consider synonyms or paraphrases as valid matches.\n"
"- Evaluate the correctness of the prediction compared to the answer."
},
{
"role": "user",
"content":
"Please evaluate the following video-based question-answer pair:\n\n"
f"Question: {question}\n"
f"Correct Answer: {answer}\n"
f"Predicted Answer: {pred}\n\n"
"Provide your evaluation only as a yes/no and score where the score is an integer value between 0 and 5, with 5 indicating the highest meaningful match. "
"Please generate the response in the form of a Python dictionary string with keys 'pred' and 'score', where value of 'pred' is a string of 'yes' or 'no' and value of 'score' is in INTEGER, not STRING."
"DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. "
"For example, your response should look like this: {'pred': 'yes', 'score': 4.8}."
}
]
completion = interaction(client, message)
# Convert response to a Python dictionary.
response_message = completion.choices[0].message.content
response_dict = ast.literal_eval(response_message)
result_qa_pair = [response_dict, qa_set]
# # Save the question-answer pairs to a json file.
with open(f"{output_dir}/{key}.json", "w") as f:
json.dump(result_qa_pair, f)
def annotate(prediction_set, caption_files, output_dir, args):
"""
Evaluates question and answer pairs using GPT-3
Returns a score for correctness.
"""
for file in tqdm(caption_files):
key = file[:-5] # Strip file extension
qa_set = prediction_set[key]
question = qa_set['q']
answer = qa_set['a']
pred = qa_set['p']
try:
prompt_gpt(question, answer, pred, key, qa_set, output_dir)
except Exception as e:
traceback.print_exc()
prompt_gpt(question, answer, pred[:50], key, qa_set, output_dir)
time.sleep(1)
def main(args):
file = open(args.pred_path)
new_pred_contents = [eval(i.strip()) for i in file.readlines()]
# Generating list of id's and corresponding files
id_list = [x['id'] for x in new_pred_contents]
caption_files = [f"{id}.json" for id in id_list]
output_dir = args.output_dir
# Generate output directory if not exists.
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Preparing dictionary of question-answer sets
prediction_set = {}
for sample in new_pred_contents:
id = sample['id']
question = sample['question']
answer = sample['answer']
pred = sample['pred']
qa_set = {"q": question, "a": answer, "p": pred}
prediction_set[id] = qa_set
num_tasks = args.num_tasks
# While loop to ensure that all captions are processed.
while True:
try:
# Files that have not been processed yet.
completed_files = os.listdir(output_dir)
print(f"completed_files: {len(completed_files)}")
# Files that have not been processed yet.
incomplete_files = [f for f in caption_files if f not in completed_files]
print(f"incomplete_files: {len(incomplete_files)}")
# Break the loop when there are no incomplete files
if len(incomplete_files) == 0:
break
if len(incomplete_files) <= num_tasks:
num_tasks = 1
# Split tasks into parts.
part_len = len(incomplete_files) // num_tasks
all_parts = [incomplete_files[i:i + part_len] for i in range(0, len(incomplete_files), part_len)]
task_args = [(prediction_set, part, args.output_dir, args) for part in all_parts]
# Use a pool of workers to process the files in parallel.
with Pool() as pool:
pool.starmap(annotate, task_args)
except Exception as e:
print(f"Error: {e}")
# Combine all the processed files into one
combined_contents = {}
json_path = args.output_json
# Iterate through json files
for file_name in tqdm(os.listdir(output_dir)):
if file_name.endswith(".json"):
file_path = os.path.join(output_dir, file_name)
with open(file_path, "r") as json_file:
try:
content = json.load(json_file)
except:
print(json_file)
exit(0)
combined_contents[file_name[:-5]] = content
# Write combined content to a json file
with open(json_path, "w") as json_file:
json.dump(combined_contents, json_file)
print("All evaluation completed!")
# Calculate average score and accuracy
score_sum = 0
count = 0
yes_count = 0
no_count = 0
for key, result in tqdm(combined_contents.items()):
try:
# Computing score
count += 1
score_match = result[0]['score']
score = int(score_match)
score_sum += score
# Computing accuracy
pred = result[0]['pred']
if "yes" in pred.lower():
yes_count += 1
elif "no" in pred.lower():
no_count += 1
except:
print(result)
average_score = score_sum / count
accuracy = yes_count / (yes_count + no_count)
print("Yes count:", yes_count)
print("No count:", no_count)
print("Accuracy:", accuracy)
print("Average score:", average_score)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
parser.add_argument("--pred-path", required=True, help="The path to file containing prediction.")
parser.add_argument("--output-dir", required=True, help="The path to save annotation json files.")
parser.add_argument("--output-json", required=True, help="The path to save annotation final combined json file.")
parser.add_argument("--num-tasks", required=True, type=int, help="Number of splits.")
parser.add_argument("--api-key", required=True, type=str, help="Azure Openai API key.")
parser.add_argument("--api-endpoint", required=True, type=str, help="Azure Openai API endpoint.")
parser.add_argument("--api-deployname", required=True, type=str, help="Azure Openai API deployname.")
args = parser.parse_args()
# Set the OpenAI API key.
os.environ["AZURE_OPENAI_KEY"] = args.api_key
os.environ["AZURE_OPENAI_ENDPOINT"] = args.api_endpoint
os.environ["AZURE_OPENAI_DEPLOYNAME"] = args.api_deployname
client = init()
main(args)